Evidence map›Paper›PMID 41673674›Full record

ArticleBMC medical education2026

Performance of ChatGPT in Israeli Arabic-language OBGYN national medical licensure exam.

Adiel Cohen, Elior Eliasi, Raanan Meyer, Yoav Brezinov, Gabriel Levin

Abstract read
In one paragraph

Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Adiel CohenDepartment of Obstetrics and Gynecology, Hadassah Medical Organization and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel. adielic@gmail.com.
Elior EliasiDepartment of Obstetrics and Gynecology, Mayanei Hayeshua Medical Center, Sackler School of Medicine, Tel Aviv University, Bnei Brak, Israel.
Raanan MeyerDepartment of Obstetrics and Gynecology, Chaim Sheba Medical Center, Ramat-Gan, Israel.
Yoav BrezinovLady Davis Institute for Cancer Research, Jewish General Hospital, McGill University, Montreal, QC, Canada.
Gabriel LevinDivision of Gynecologic Oncology, McGill University Health Centre, McGill University, Montreal, QC, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious studies of ChatGPT performance in the field of medical exams have reached contradictory results. The performance of ChatGPT in languages other than English, including Arabic, which is the official language of medical education and practice in many countries, has yet to be explored. We aim to evaluate the performance of ChatGPT in Arabic-language Israeli OBGYN medical licensure exams for foreign university alumni.

methodsWe conducted a performance study using a consecutive sample of text-based multiple-choice questions, originated from authentic Arabic-language Israeli OBGYN medical licensure exams for foreign university alumni. ChatGPT-3.5 (using a newly created account) answered all questions in Arabic. We compared the performance of ChatGPT including in the different fields of the exam; Obstetrics, Reproductive medicine and Infertility, Gynecology and Gynecologic Oncology, and also compared ChatGPT Arabic performance vs. previously published English medical tests.

resultsOverall, 123 authentic questions were analyzed. ChatGPT correctly answered 54 questions (43.9%, 95% CI: 35.1% – 52.7%) and reached a score below 50%. There was no difference in ChatGPT performance in the four different subjects of the exam: Gynecologic Oncology (61.5%, 95% CI: 35.1% – 87.9%), Gynecology (44.0%, 95% CI: 24.5% – 63.5%), Obstetrics (42.3%, 95% CI: 28.9% – 55.7%), Reproductive medicine and Infertility (39.4%, 95% CI: 22.7% – 56.1%), p = .579. In a comparison to ChatGPT performance in 9,091 English language questions in the field of medicine, the performance of Arabic ChatGPT was lower (43.9% in Arabic vs. 60.7% in English, p < .001).

conclusionsChatGPT-3.5 answered correctly approximately 44% of Arabic OBGYN medical licensure exam questions. At the time of writing of this manuscript, considering the results of our analysis, ChatGPT-3.5 cannot be considered a reliable primary tool for exam preparation in Arabic. Further research and efforts should be made to improve ChatGPT performance in other languages besides English especially Arabic.

Indexed as

Educational MeasurementGynecologyLanguageLicensure, MedicalObstetricsFemaleHumansIsraelLarge Language ModelsArabicChatGPTMedical examOBGYNPerformance

Identifiers

PMID41673674
PMCPMC12998313

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.